Scaling In-Context Segmentation with Hierarchical Supervision

TL;DR

PatchICL introduces a hierarchical, selective attention framework for medical image segmentation that reduces computation and improves out-of-domain performance by focusing on informative regions.

Contribution

It presents a novel hierarchical approach combining patch selection and multi-level supervision, enhancing efficiency and generalization in in-context segmentation.

Findings

Reduces compute by 44% at 512×512 resolution compared to baseline.

Outperforms baseline on 6 of 13 out-of-domain modalities.

Achieves competitive accuracy on in-domain CT segmentation.

Abstract

In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical annotation burden. However, standard ICL methods typically rely on dense, global cross-attention, which scales poorly with image resolution. While recent approaches have introduced localized attention mechanisms, they often lack explicit supervision on the selection process, leading to redundant computation in non-informative regions. We propose PatchICL, a hierarchical framework that combines selective image patching with multi-level supervision. Our approach learns to actively identify and attend only to the most informative anatomical regions. Compared to UniverSeg, a strong global-attention baseline, PatchICL achieves competitive in-domain CT segmentation accuracy while reducing compute by 44\% at $512\times512$ resolution. On 35 out-of-domain datasets spanning diverse imaging modalities, PatchICL outperforms the baseline on 6 of 13 modality categories, with particular strength on modalities dominated by localized pathology such as OCT and dermoscopy. Training and evaluation code are available at https://github.com/tidiane-camaret/ic_segmentation